10 citations · 11 across the 3 of their papers we have counts for
5 papers
SCNet: Enhancing Few-Shot Semantic Segmentation by Self-Contrastive Background Prototypes
Jiacheng Chen, Bin-Bin Gao, Zongqing Lu +3
Few-shot semantic segmentation aims to segment novel-class objects in a query image with only a few annotated examples in support images. Most of advanced solutions exploit a metri…
A region-based descriptor network for uniformly sampled keypoints
Kai Lv, Zongqing Lu, Qingmin Liao
Matching keypoint pairs of different images is a basic task of computer vision. Most methods require customized extremum point schemes to obtain the coordinates of feature points w…
Noise-Sampling Cross Entropy Loss: Improving Disparity Regression Via Cost Volume Aware Regularizer
Yang Chen, Zongqing Lu, Xuechen Zhang +2
Recent end-to-end deep neural networks for disparity regression have achieved the state-of-the-art performance. However, many well-acknowledged specific properties of disparity est…
XSepConv: Extremely Separated Convolution
Jiarong Chen, Zongqing Lu, Jing-Hao Xue +1
Depthwise convolution has gradually become an indispensable operation for modern efficient neural networks and larger kernel sizes () have been applied to it recently. In thi…
Optical Flow Super-Resolution Based on Image Guidence Using Convolutional Neural Network
Liping Zhang, Zongqing Lu, Qingmin Liao
The convolutional neural network model for optical flow estimation usually outputs a low-resolution(LR) optical flow field. To obtain the corresponding full image resolution,interp…